In noisy environment, performance of speech recognition systems trained in
quiet environment is degraded. One of the reasons is acoustic phonetic
modification caused by the Lombard effect, another is noise contamination of
speech signal. This paper presents a new method for isolated word recognition in
noisy environment. The method is based on two techniques. One of them is based
on variability models for acoustic phonetic modification in Lombard speech, and
another is to estimate additive noise spectrum frame by frame. The acoustic
phonetic variability models represent the spectral difference between normal
speech and Lombard speech. Each model is comprised of a nonlinear warping
function on spectral domain and two spectral filters. The warping function
represents formant shift. Two filters do the changes of formant bandwidth and of
spectral tilt. The noise estimation is executed for each frame of noisy input
with noise models and speech models made with clean speech data. These
techniques were applied to speaker-dependent word recognition based on
continuous density HMMs of subphoneme. Experimental evaluations were executed
with the noisy Lombard speech data of 100 isolated words. From the experiments,
the effectiveness of the proposed method has been confirmed.